Overview

Dataset statistics

Number of variables18
Number of observations3315
Missing cells25432
Missing cells (%)42.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory466.3 KiB
Average record size in memory144.0 B

Variable types

Categorical4
Numeric14

Warnings

name has a high cardinality: 51 distinct values High cardinality
abbreviation has a high cardinality: 51 distinct values High cardinality
as_of_date has a high cardinality: 353 distinct values High cardinality
notes has a high cardinality: 597 distinct values High cardinality
staff_tests is highly correlated with total_staff_cases and 4 other fieldsHigh correlation
staff_tests_with_multiples is highly correlated with total_staff_cases and 10 other fieldsHigh correlation
total_staff_cases is highly correlated with staff_tests and 12 other fieldsHigh correlation
staff_recovered is highly correlated with staff_tests_with_multiples and 11 other fieldsHigh correlation
total_staff_deaths is highly correlated with staff_tests_with_multiples and 10 other fieldsHigh correlation
staff_partial_dose is highly correlated with staff_tests_with_multiples and 11 other fieldsHigh correlation
staff_full_dose is highly correlated with staff_tests and 12 other fieldsHigh correlation
prisoner_tests is highly correlated with staff_tests and 12 other fieldsHigh correlation
prisoner_tests_with_multiples is highly correlated with staff_tests_with_multiples and 10 other fieldsHigh correlation
total_prisoner_cases is highly correlated with staff_tests and 12 other fieldsHigh correlation
prisoners_recovered is highly correlated with staff_tests and 12 other fieldsHigh correlation
total_prisoner_deaths is highly correlated with staff_tests_with_multiples and 11 other fieldsHigh correlation
prisoners_partial_dose is highly correlated with total_staff_cases and 9 other fieldsHigh correlation
prisoners_full_dose is highly correlated with staff_tests_with_multiples and 10 other fieldsHigh correlation
staff_tests is highly correlated with staff_tests_with_multiples and 7 other fieldsHigh correlation
staff_tests_with_multiples is highly correlated with staff_tests and 10 other fieldsHigh correlation
total_staff_cases is highly correlated with staff_tests and 9 other fieldsHigh correlation
staff_recovered is highly correlated with staff_tests and 9 other fieldsHigh correlation
total_staff_deaths is highly correlated with staff_tests_with_multiples and 7 other fieldsHigh correlation
staff_partial_dose is highly correlated with staff_tests_with_multiples and 7 other fieldsHigh correlation
staff_full_dose is highly correlated with staff_tests_with_multiples and 4 other fieldsHigh correlation
prisoner_tests is highly correlated with staff_tests and 12 other fieldsHigh correlation
prisoner_tests_with_multiples is highly correlated with staff_tests and 9 other fieldsHigh correlation
total_prisoner_cases is highly correlated with staff_tests and 8 other fieldsHigh correlation
prisoners_recovered is highly correlated with staff_tests and 8 other fieldsHigh correlation
total_prisoner_deaths is highly correlated with staff_tests and 8 other fieldsHigh correlation
prisoners_partial_dose is highly correlated with staff_partial_dose and 3 other fieldsHigh correlation
prisoners_full_dose is highly correlated with staff_partial_dose and 3 other fieldsHigh correlation
staff_tests is highly correlated with staff_tests_with_multiples and 7 other fieldsHigh correlation
staff_tests_with_multiples is highly correlated with staff_tests and 7 other fieldsHigh correlation
total_staff_cases is highly correlated with staff_tests and 8 other fieldsHigh correlation
staff_recovered is highly correlated with staff_tests and 8 other fieldsHigh correlation
total_staff_deaths is highly correlated with total_staff_cases and 4 other fieldsHigh correlation
staff_partial_dose is highly correlated with staff_full_dose and 3 other fieldsHigh correlation
staff_full_dose is highly correlated with staff_partial_dose and 3 other fieldsHigh correlation
prisoner_tests is highly correlated with staff_tests and 11 other fieldsHigh correlation
prisoner_tests_with_multiples is highly correlated with staff_tests and 7 other fieldsHigh correlation
total_prisoner_cases is highly correlated with staff_tests and 8 other fieldsHigh correlation
prisoners_recovered is highly correlated with staff_tests and 8 other fieldsHigh correlation
total_prisoner_deaths is highly correlated with staff_tests and 8 other fieldsHigh correlation
prisoners_partial_dose is highly correlated with staff_partial_dose and 3 other fieldsHigh correlation
prisoners_full_dose is highly correlated with staff_partial_dose and 3 other fieldsHigh correlation
staff_tests_with_multiples is highly correlated with staff_recovered and 13 other fieldsHigh correlation
staff_recovered is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
name is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
prisoner_tests is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
staff_full_dose is highly correlated with staff_tests_with_multiples and 13 other fieldsHigh correlation
prisoners_full_dose is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
staff_tests is highly correlated with staff_recovered and 10 other fieldsHigh correlation
prisoners_recovered is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
total_prisoner_cases is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
total_prisoner_deaths is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
prisoner_tests_with_multiples is highly correlated with staff_tests_with_multiples and 13 other fieldsHigh correlation
prisoners_partial_dose is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
total_staff_deaths is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
staff_partial_dose is highly correlated with staff_tests_with_multiples and 13 other fieldsHigh correlation
total_staff_cases is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
abbreviation is highly correlated with staff_tests_with_multiples and 14 other fieldsHigh correlation
name is highly correlated with abbreviationHigh correlation
abbreviation is highly correlated with nameHigh correlation
staff_tests has 2925 (88.2%) missing values Missing
staff_tests_with_multiples has 2657 (80.2%) missing values Missing
total_staff_cases has 284 (8.6%) missing values Missing
staff_recovered has 1253 (37.8%) missing values Missing
total_staff_deaths has 500 (15.1%) missing values Missing
staff_partial_dose has 2732 (82.4%) missing values Missing
staff_full_dose has 2832 (85.4%) missing values Missing
prisoner_tests has 2002 (60.4%) missing values Missing
prisoner_tests_with_multiples has 1723 (52.0%) missing values Missing
total_prisoner_cases has 143 (4.3%) missing values Missing
prisoners_recovered has 1072 (32.3%) missing values Missing
total_prisoner_deaths has 131 (4.0%) missing values Missing
prisoners_partial_dose has 2523 (76.1%) missing values Missing
prisoners_full_dose has 2625 (79.2%) missing values Missing
notes has 2002 (60.4%) missing values Missing
name is uniformly distributed Uniform
abbreviation is uniformly distributed Uniform
total_staff_cases has 60 (1.8%) zeros Zeros
total_staff_deaths has 1396 (42.1%) zeros Zeros
staff_partial_dose has 161 (4.9%) zeros Zeros
staff_full_dose has 198 (6.0%) zeros Zeros
total_prisoner_cases has 164 (4.9%) zeros Zeros
prisoners_recovered has 44 (1.3%) zeros Zeros
total_prisoner_deaths has 715 (21.6%) zeros Zeros
prisoners_partial_dose has 283 (8.5%) zeros Zeros
prisoners_full_dose has 339 (10.2%) zeros Zeros

Reproduction

Analysis started2021-06-30 19:34:53.198555
Analysis finished2021-06-30 19:35:12.709100
Duration19.51 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Pennsylvania
 
65
Missouri
 
65
Nevada
 
65
Hawaii
 
65
Alabama
 
65
Other values (46)
2990 

Length

Max length14
Median length8
Mean length8.411764706
Min length4

Characters and Unicode

Total characters27885
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlaska
3rd rowArizona
4th rowArkansas
5th rowCalifornia

Common Values

ValueCountFrequency (%)
Pennsylvania65
 
2.0%
Missouri65
 
2.0%
Nevada65
 
2.0%
Hawaii65
 
2.0%
Alabama65
 
2.0%
California65
 
2.0%
Oregon65
 
2.0%
Massachusetts65
 
2.0%
Illinois65
 
2.0%
North Carolina65
 
2.0%
Other values (41)2665
80.4%

Length

2021-06-30T15:35:12.915879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new260
 
6.6%
carolina130
 
3.3%
south130
 
3.3%
north130
 
3.3%
virginia130
 
3.3%
dakota130
 
3.3%
colorado65
 
1.6%
vermont65
 
1.6%
mexico65
 
1.6%
nevada65
 
1.6%
Other values (43)2795
70.5%

Most occurring characters

ValueCountFrequency (%)
a3770
13.5%
i2535
 
9.1%
n2275
 
8.2%
o2145
 
7.7%
s1950
 
7.0%
e1950
 
7.0%
r1430
 
5.1%
t1105
 
4.0%
l975
 
3.5%
h845
 
3.0%
Other values (36)8905
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23270
83.4%
Uppercase Letter3965
 
14.2%
Space Separator650
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3770
16.2%
i2535
10.9%
n2275
9.8%
o2145
9.2%
s1950
8.4%
e1950
8.4%
r1430
 
6.1%
t1105
 
4.7%
l975
 
4.2%
h845
 
3.6%
Other values (14)4290
18.4%
Uppercase Letter
ValueCountFrequency (%)
M585
14.8%
N520
13.1%
C325
 
8.2%
I325
 
8.2%
A260
 
6.6%
W260
 
6.6%
D195
 
4.9%
O195
 
4.9%
V195
 
4.9%
F130
 
3.3%
Other values (11)975
24.6%
Space Separator
ValueCountFrequency (%)
650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27235
97.7%
Common650
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3770
13.8%
i2535
 
9.3%
n2275
 
8.4%
o2145
 
7.9%
s1950
 
7.2%
e1950
 
7.2%
r1430
 
5.3%
t1105
 
4.1%
l975
 
3.6%
h845
 
3.1%
Other values (35)8255
30.3%
Common
ValueCountFrequency (%)
650
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII27885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3770
13.5%
i2535
 
9.1%
n2275
 
8.2%
o2145
 
7.7%
s1950
 
7.0%
e1950
 
7.0%
r1430
 
5.1%
t1105
 
4.0%
l975
 
3.5%
h845
 
3.0%
Other values (36)8905
31.9%

abbreviation
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
IL
 
65
MD
 
65
CO
 
65
LA
 
65
DE
 
65
Other values (46)
2990 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6630
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAL
2nd rowAK
3rd rowAZ
4th rowAR
5th rowCA

Common Values

ValueCountFrequency (%)
IL65
 
2.0%
MD65
 
2.0%
CO65
 
2.0%
LA65
 
2.0%
DE65
 
2.0%
GA65
 
2.0%
ME65
 
2.0%
OH65
 
2.0%
MA65
 
2.0%
NE65
 
2.0%
Other values (41)2665
80.4%

Length

2021-06-30T15:35:13.099218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ut65
 
2.0%
wi65
 
2.0%
ct65
 
2.0%
ny65
 
2.0%
ky65
 
2.0%
ks65
 
2.0%
la65
 
2.0%
md65
 
2.0%
nh65
 
2.0%
de65
 
2.0%
Other values (41)2665
80.4%

Most occurring characters

ValueCountFrequency (%)
A780
 
11.8%
N715
 
10.8%
M585
 
8.8%
I520
 
7.8%
T390
 
5.9%
C325
 
4.9%
O325
 
4.9%
D325
 
4.9%
S325
 
4.9%
L260
 
3.9%
Other values (14)2080
31.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A780
 
11.8%
N715
 
10.8%
M585
 
8.8%
I520
 
7.8%
T390
 
5.9%
C325
 
4.9%
O325
 
4.9%
D325
 
4.9%
S325
 
4.9%
L260
 
3.9%
Other values (14)2080
31.4%

Most occurring scripts

ValueCountFrequency (%)
Latin6630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A780
 
11.8%
N715
 
10.8%
M585
 
8.8%
I520
 
7.8%
T390
 
5.9%
C325
 
4.9%
O325
 
4.9%
D325
 
4.9%
S325
 
4.9%
L260
 
3.9%
Other values (14)2080
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII6630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A780
 
11.8%
N715
 
10.8%
M585
 
8.8%
I520
 
7.8%
T390
 
5.9%
C325
 
4.9%
O325
 
4.9%
D325
 
4.9%
S325
 
4.9%
L260
 
3.9%
Other values (14)2080
31.4%

staff_tests
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct315
Distinct (%)80.8%
Missing2925
Missing (%)88.2%
Infinite0
Infinite (%)0.0%
Mean1747.961538
Minimum0
Maximum14297
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:13.180395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.9
Q1251.25
median1023.5
Q32398.25
95-th percentile5983.65
Maximum14297
Range14297
Interquartile range (IQR)2147

Descriptive statistics

Standard deviation2083.745365
Coefficient of variation (CV)1.192100237
Kurtosis6.11604026
Mean1747.961538
Median Absolute Deviation (MAD)934.5
Skewness2.13268807
Sum681705
Variance4341994.747
MonotonicityNot monotonic
2021-06-30T15:35:13.274078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48657
 
0.2%
2507
 
0.2%
05
 
0.2%
10304
 
0.1%
5714
 
0.1%
4644
 
0.1%
103
 
0.1%
22483
 
0.1%
11473
 
0.1%
643
 
0.1%
Other values (305)347
 
10.5%
(Missing)2925
88.2%
ValueCountFrequency (%)
05
0.2%
12
 
0.1%
21
 
< 0.1%
61
 
< 0.1%
72
 
0.1%
81
 
< 0.1%
103
0.1%
121
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
142971
< 0.1%
124481
< 0.1%
106011
< 0.1%
92451
< 0.1%
87281
< 0.1%
83931
< 0.1%
80201
< 0.1%
75252
0.1%
68261
< 0.1%
64811
< 0.1%

staff_tests_with_multiples
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct609
Distinct (%)92.6%
Missing2657
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean43640.10182
Minimum89
Maximum278704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:13.370936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile421.85
Q14905
median12962.5
Q358968
95-th percentile195841.05
Maximum278704
Range278615
Interquartile range (IQR)54063

Descriptive statistics

Standard deviation62817.01126
Coefficient of variation (CV)1.439433196
Kurtosis2.461072836
Mean43640.10182
Median Absolute Deviation (MAD)11327
Skewness1.824615741
Sum28715187
Variance3945976904
MonotonicityNot monotonic
2021-06-30T15:35:13.464542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49057
 
0.2%
5736
 
0.2%
4645
 
0.2%
76435
 
0.2%
4244
 
0.1%
172864
 
0.1%
217663
 
0.1%
130022
 
0.1%
4092
 
0.1%
364902
 
0.1%
Other values (599)618
 
18.6%
(Missing)2657
80.2%
ValueCountFrequency (%)
891
< 0.1%
1421
< 0.1%
1591
< 0.1%
1771
< 0.1%
1941
< 0.1%
1982
0.1%
2821
< 0.1%
2981
< 0.1%
3211
< 0.1%
3451
< 0.1%
ValueCountFrequency (%)
2787041
< 0.1%
2757011
< 0.1%
2642871
< 0.1%
2585721
< 0.1%
2561001
< 0.1%
2534061
< 0.1%
2473531
< 0.1%
2468681
< 0.1%
2461551
< 0.1%
2429431
< 0.1%

total_staff_cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1480
Distinct (%)48.8%
Missing284
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean1096.181788
Minimum0
Maximum17002
Zeros60
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:13.563500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q192
median377
Q31119
95-th percentile4603.5
Maximum17002
Range17002
Interquartile range (IQR)1027

Descriptive statistics

Standard deviation2031.927873
Coefficient of variation (CV)1.853641335
Kurtosis23.02790925
Mean1096.181788
Median Absolute Deviation (MAD)360
Skewness4.226232458
Sum3322527
Variance4128730.88
MonotonicityNot monotonic
2021-06-30T15:35:13.660842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060
 
1.8%
152
 
1.6%
1141
 
1.2%
437
 
1.1%
332
 
1.0%
522
 
0.7%
222
 
0.7%
919
 
0.6%
1617
 
0.5%
2017
 
0.5%
Other values (1470)2712
81.8%
(Missing)284
 
8.6%
ValueCountFrequency (%)
060
1.8%
152
1.6%
222
 
0.7%
332
1.0%
437
1.1%
522
 
0.7%
68
 
0.2%
79
 
0.3%
816
 
0.5%
919
 
0.6%
ValueCountFrequency (%)
170021
< 0.1%
168151
< 0.1%
167701
< 0.1%
165371
< 0.1%
164851
< 0.1%
164411
< 0.1%
163841
< 0.1%
163231
< 0.1%
162841
< 0.1%
160371
< 0.1%

staff_recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1189
Distinct (%)57.7%
Missing1253
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean1205.332687
Minimum0
Maximum16941
Zeros22
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:13.762950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q1131.25
median435
Q31253.75
95-th percentile4806.95
Maximum16941
Range16941
Interquartile range (IQR)1122.5

Descriptive statistics

Standard deviation2175.448181
Coefficient of variation (CV)1.804852888
Kurtosis20.02524583
Mean1205.332687
Median Absolute Deviation (MAD)388
Skewness3.998374801
Sum2485396
Variance4732574.789
MonotonicityNot monotonic
2021-06-30T15:35:14.145830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1128
 
0.8%
425
 
0.8%
022
 
0.7%
119
 
0.6%
515
 
0.5%
1910
 
0.3%
39610
 
0.3%
1210
 
0.3%
19110
 
0.3%
279
 
0.3%
Other values (1179)1904
57.4%
(Missing)1253
37.8%
ValueCountFrequency (%)
022
0.7%
119
0.6%
25
 
0.2%
37
 
0.2%
425
0.8%
515
0.5%
62
 
0.1%
72
 
0.1%
86
 
0.2%
94
 
0.1%
ValueCountFrequency (%)
169411
< 0.1%
166941
< 0.1%
166111
< 0.1%
164031
< 0.1%
163311
< 0.1%
162611
< 0.1%
162011
< 0.1%
161371
< 0.1%
160841
< 0.1%
157061
< 0.1%

total_staff_deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct38
Distinct (%)1.3%
Missing500
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean2.328241563
Minimum0
Maximum48
Zeros1396
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:14.242801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum48
Range48
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.244169768
Coefficient of variation (CV)2.252416524
Kurtosis34.02607846
Mean2.328241563
Median Absolute Deviation (MAD)1
Skewness5.247781437
Sum6554
Variance27.50131656
MonotonicityNot monotonic
2021-06-30T15:35:14.336135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
01396
42.1%
1366
 
11.0%
2311
 
9.4%
4160
 
4.8%
3158
 
4.8%
5155
 
4.7%
678
 
2.4%
1031
 
0.9%
826
 
0.8%
722
 
0.7%
Other values (28)112
 
3.4%
(Missing)500
 
15.1%
ValueCountFrequency (%)
01396
42.1%
1366
 
11.0%
2311
 
9.4%
3158
 
4.8%
4160
 
4.8%
5155
 
4.7%
678
 
2.4%
722
 
0.7%
826
 
0.8%
921
 
0.6%
ValueCountFrequency (%)
483
0.1%
471
 
< 0.1%
465
0.2%
452
 
0.1%
441
 
< 0.1%
433
0.1%
412
 
0.1%
401
 
< 0.1%
381
 
< 0.1%
372
 
0.1%

staff_partial_dose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct301
Distinct (%)51.6%
Missing2732
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean2847.586621
Minimum0
Maximum35115
Zeros161
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:14.430782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1049
Q33126.5
95-th percentile11852.7
Maximum35115
Range35115
Interquartile range (IQR)3126.5

Descriptive statistics

Standard deviation5500.532338
Coefficient of variation (CV)1.931647065
Kurtosis15.02727724
Mean2847.586621
Median Absolute Deviation (MAD)1049
Skewness3.708219643
Sum1660143
Variance30255856
MonotonicityNot monotonic
2021-06-30T15:35:14.526140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0161
 
4.9%
269711
 
0.3%
120810
 
0.3%
8349
 
0.3%
40119
 
0.3%
228
 
0.2%
15178
 
0.2%
2497
 
0.2%
75466
 
0.2%
2246
 
0.2%
Other values (291)348
 
10.5%
(Missing)2732
82.4%
ValueCountFrequency (%)
0161
4.9%
41
 
< 0.1%
161
 
< 0.1%
228
 
0.2%
301
 
< 0.1%
461
 
< 0.1%
501
 
< 0.1%
1001
 
< 0.1%
1071
 
< 0.1%
1132
 
0.1%
ValueCountFrequency (%)
351151
< 0.1%
348371
< 0.1%
332591
< 0.1%
332311
< 0.1%
320251
< 0.1%
316421
< 0.1%
279681
< 0.1%
278441
< 0.1%
278061
< 0.1%
275391
< 0.1%

staff_full_dose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct199
Distinct (%)41.2%
Missing2832
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean2660.146998
Minimum0
Maximum33060
Zeros198
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:14.626324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median467
Q32824
95-th percentile16410.1
Maximum33060
Range33060
Interquartile range (IQR)2824

Descriptive statistics

Standard deviation5521.497895
Coefficient of variation (CV)2.075636384
Kurtosis11.42173549
Mean2660.146998
Median Absolute Deviation (MAD)467
Skewness3.272595409
Sum1284851
Variance30486939
MonotonicityNot monotonic
2021-06-30T15:35:14.719335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0198
 
6.0%
46715
 
0.5%
396810
 
0.3%
1437
 
0.2%
2137
 
0.2%
8126
 
0.2%
42726
 
0.2%
7805
 
0.2%
11994
 
0.1%
5424
 
0.1%
Other values (189)221
 
6.7%
(Missing)2832
85.4%
ValueCountFrequency (%)
0198
6.0%
221
 
< 0.1%
1091
 
< 0.1%
1101
 
< 0.1%
1191
 
< 0.1%
1331
 
< 0.1%
1437
 
0.2%
1581
 
< 0.1%
2137
 
0.2%
2142
 
0.1%
ValueCountFrequency (%)
330601
< 0.1%
324991
< 0.1%
308401
< 0.1%
308001
< 0.1%
292401
< 0.1%
288551
< 0.1%
262071
< 0.1%
259311
< 0.1%
258441
< 0.1%
252821
< 0.1%

prisoner_tests
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1146
Distinct (%)87.3%
Missing2002
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean15819.31379
Minimum0
Maximum128746
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:14.815446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.6
Q1720
median3676
Q317941
95-th percentile89512.2
Maximum128746
Range128746
Interquartile range (IQR)17221

Descriptive statistics

Standard deviation26804.34284
Coefficient of variation (CV)1.694406167
Kurtosis6.171124142
Mean15819.31379
Median Absolute Deviation (MAD)3636
Skewness2.524820375
Sum20770759
Variance718472795
MonotonicityNot monotonic
2021-06-30T15:35:14.916179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
0.2%
36768
 
0.2%
57
 
0.2%
177
 
0.2%
346
 
0.2%
146
 
0.2%
106
 
0.2%
155
 
0.2%
275
 
0.2%
7234
 
0.1%
Other values (1136)1251
37.7%
(Missing)2002
60.4%
ValueCountFrequency (%)
08
0.2%
13
 
0.1%
22
 
0.1%
33
 
0.1%
44
0.1%
57
0.2%
61
 
< 0.1%
72
 
0.1%
82
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
1287461
< 0.1%
1279991
< 0.1%
1273051
< 0.1%
1269211
< 0.1%
1262081
< 0.1%
1255411
< 0.1%
1245531
< 0.1%
1233561
< 0.1%
1228011
< 0.1%
1220711
< 0.1%

prisoner_tests_with_multiples
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1540
Distinct (%)96.7%
Missing1723
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean61814.40578
Minimum2
Maximum978781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.017850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1425.25
Q17647.25
median23441
Q362273.25
95-th percentile259624.1
Maximum978781
Range978779
Interquartile range (IQR)54626

Descriptive statistics

Standard deviation117715.6865
Coefficient of variation (CV)1.904340663
Kurtosis19.43254406
Mean61814.40578
Median Absolute Deviation (MAD)18061
Skewness4.061564116
Sum98408534
Variance1.385698284 × 1010
MonotonicityNot monotonic
2021-06-30T15:35:15.122338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183484
 
0.1%
340063
 
0.1%
67843
 
0.1%
534523
 
0.1%
67823
 
0.1%
3152
 
0.1%
34482
 
0.1%
291522
 
0.1%
22822
 
0.1%
339072
 
0.1%
Other values (1530)1566
47.2%
(Missing)1723
52.0%
ValueCountFrequency (%)
21
< 0.1%
61
< 0.1%
81
< 0.1%
111
< 0.1%
181
< 0.1%
201
< 0.1%
651
< 0.1%
681
< 0.1%
701
< 0.1%
741
< 0.1%
ValueCountFrequency (%)
9787811
< 0.1%
9526211
< 0.1%
9298061
< 0.1%
9098191
< 0.1%
8814581
< 0.1%
8492191
< 0.1%
8143141
< 0.1%
7883211
< 0.1%
7609431
< 0.1%
7275431
< 0.1%

total_prisoner_cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct2081
Distinct (%)65.6%
Missing143
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean3963.015448
Minimum0
Maximum49375
Zeros164
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.222231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1241
median1399
Q34496.25
95-th percentile16436.1
Maximum49375
Range49375
Interquartile range (IQR)4255.25

Descriptive statistics

Standard deviation7091.46306
Coefficient of variation (CV)1.789410905
Kurtosis17.42187856
Mean3963.015448
Median Absolute Deviation (MAD)1388
Skewness3.788603738
Sum12570685
Variance50288848.33
MonotonicityNot monotonic
2021-06-30T15:35:15.324656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0164
 
4.9%
152
 
1.6%
441
 
1.2%
240
 
1.2%
1017
 
0.5%
315
 
0.5%
715
 
0.5%
514
 
0.4%
24014
 
0.4%
1112
 
0.4%
Other values (2071)2788
84.1%
(Missing)143
 
4.3%
ValueCountFrequency (%)
0164
4.9%
152
 
1.6%
240
 
1.2%
315
 
0.5%
441
 
1.2%
514
 
0.4%
62
 
0.1%
715
 
0.5%
89
 
0.3%
911
 
0.3%
ValueCountFrequency (%)
493751
< 0.1%
493561
< 0.1%
493241
< 0.1%
492981
< 0.1%
492541
< 0.1%
492371
< 0.1%
492291
< 0.1%
492211
< 0.1%
492151
< 0.1%
492141
< 0.1%

prisoners_recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1649
Distinct (%)73.5%
Missing1072
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean4434.114579
Minimum0
Maximum48537
Zeros44
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.424284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1440.5
median1692
Q34777.5
95-th percentile17851.9
Maximum48537
Range48537
Interquartile range (IQR)4337

Descriptive statistics

Standard deviation7726.994307
Coefficient of variation (CV)1.74262396
Kurtosis14.27330405
Mean4434.114579
Median Absolute Deviation (MAD)1555
Skewness3.514692354
Sum9945719
Variance59706441.02
MonotonicityNot monotonic
2021-06-30T15:35:15.525405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044
 
1.3%
429
 
0.9%
127
 
0.8%
1014
 
0.4%
212
 
0.4%
23811
 
0.3%
510
 
0.3%
810
 
0.3%
6339
 
0.3%
279
 
0.3%
Other values (1639)2068
62.4%
(Missing)1072
32.3%
ValueCountFrequency (%)
044
1.3%
127
0.8%
212
 
0.4%
35
 
0.2%
429
0.9%
510
 
0.3%
62
 
0.1%
76
 
0.2%
810
 
0.3%
93
 
0.1%
ValueCountFrequency (%)
485371
< 0.1%
484601
< 0.1%
484561
< 0.1%
484041
< 0.1%
483961
< 0.1%
483951
< 0.1%
483901
< 0.1%
483861
< 0.1%
483821
< 0.1%
483791
< 0.1%

total_prisoner_deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct203
Distinct (%)6.4%
Missing131
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean30.26853015
Minimum0
Maximum273
Zeros715
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.619880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10.5
Q338
95-th percentile136
Maximum273
Range273
Interquartile range (IQR)37

Descriptive statistics

Standard deviation50.08219887
Coefficient of variation (CV)1.654596329
Kurtosis8.004226527
Mean30.26853015
Median Absolute Deviation (MAD)10.5
Skewness2.753307714
Sum96375
Variance2508.226644
MonotonicityNot monotonic
2021-06-30T15:35:15.709816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0715
21.6%
1165
 
5.0%
2136
 
4.1%
398
 
3.0%
696
 
2.9%
487
 
2.6%
772
 
2.2%
869
 
2.1%
1368
 
2.1%
568
 
2.1%
Other values (193)1610
48.6%
(Missing)131
 
4.0%
ValueCountFrequency (%)
0715
21.6%
1165
 
5.0%
2136
 
4.1%
398
 
3.0%
487
 
2.6%
568
 
2.1%
696
 
2.9%
772
 
2.2%
869
 
2.1%
946
 
1.4%
ValueCountFrequency (%)
27313
0.4%
2712
 
0.1%
2701
 
< 0.1%
2681
 
< 0.1%
2671
 
< 0.1%
2601
 
< 0.1%
2562
 
0.1%
2552
 
0.1%
2532
 
0.1%
2522
 
0.1%

prisoners_partial_dose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct426
Distinct (%)53.8%
Missing2523
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean4697.820707
Minimum0
Maximum70794
Zeros283
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.806686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median802
Q34670.5
95-th percentile18394.95
Maximum70794
Range70794
Interquartile range (IQR)4670.5

Descriptive statistics

Standard deviation10181.72401
Coefficient of variation (CV)2.167329203
Kurtosis22.18288773
Mean4697.820707
Median Absolute Deviation (MAD)802
Skewness4.338825928
Sum3720674
Variance103667503.8
MonotonicityNot monotonic
2021-06-30T15:35:15.899973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0283
 
8.5%
1979
 
0.3%
5746
 
0.2%
21646
 
0.2%
7295
 
0.2%
108444
 
0.1%
8034
 
0.1%
43624
 
0.1%
39384
 
0.1%
3583
 
0.1%
Other values (416)464
 
14.0%
(Missing)2523
76.1%
ValueCountFrequency (%)
0283
8.5%
82
 
0.1%
301
 
< 0.1%
401
 
< 0.1%
791
 
< 0.1%
821
 
< 0.1%
901
 
< 0.1%
1001
 
< 0.1%
1041
 
< 0.1%
1092
 
0.1%
ValueCountFrequency (%)
707941
< 0.1%
703741
< 0.1%
699461
< 0.1%
697981
< 0.1%
691651
< 0.1%
681071
< 0.1%
676581
< 0.1%
668231
< 0.1%
666011
< 0.1%
657281
< 0.1%

prisoners_full_dose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct293
Distinct (%)42.5%
Missing2625
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean4677.82029
Minimum0
Maximum78858
Zeros339
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2021-06-30T15:35:15.999043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median87
Q33585
95-th percentile20118
Maximum78858
Range78858
Interquartile range (IQR)3585

Descriptive statistics

Standard deviation11762.40905
Coefficient of variation (CV)2.514506398
Kurtosis17.26355825
Mean4677.82029
Median Absolute Deviation (MAD)87
Skewness3.971358369
Sum3227696
Variance138354266.6
MonotonicityNot monotonic
2021-06-30T15:35:16.103809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0339
 
10.2%
1889510
 
0.3%
2846
 
0.2%
87344
 
0.1%
7234
 
0.1%
1974
 
0.1%
10114
 
0.1%
5374
 
0.1%
42793
 
0.1%
44013
 
0.1%
Other values (283)309
 
9.3%
(Missing)2625
79.2%
ValueCountFrequency (%)
0339
10.2%
21
 
< 0.1%
141
 
< 0.1%
261
 
< 0.1%
821
 
< 0.1%
852
 
0.1%
891
 
< 0.1%
921
 
< 0.1%
1061
 
< 0.1%
1101
 
< 0.1%
ValueCountFrequency (%)
788581
< 0.1%
756761
< 0.1%
730501
< 0.1%
691811
< 0.1%
688051
< 0.1%
681111
< 0.1%
674811
< 0.1%
673291
< 0.1%
665551
< 0.1%
656261
< 0.1%

as_of_date
Categorical

HIGH CARDINALITY

Distinct353
Distinct (%)10.7%
Missing28
Missing (%)0.8%
Memory size26.0 KiB
03/26/2020
 
47
04/01/2020
 
46
10/20/2020
 
44
05/20/2020
 
44
08/18/2020
 
44
Other values (348)
3062 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters32870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)3.1%

Sample

1st row06/16/2021
2nd row06/15/2021
3rd row06/16/2021
4th row06/15/2021
5th row06/16/2021

Common Values

ValueCountFrequency (%)
03/26/202047
 
1.4%
04/01/202046
 
1.4%
10/20/202044
 
1.3%
05/20/202044
 
1.3%
08/18/202044
 
1.3%
12/22/202043
 
1.3%
05/13/202043
 
1.3%
04/15/202042
 
1.3%
09/08/202042
 
1.3%
04/22/202042
 
1.3%
Other values (343)2850
86.0%

Length

2021-06-30T15:35:16.311632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03/26/202047
 
1.4%
04/01/202046
 
1.4%
08/18/202044
 
1.3%
10/20/202044
 
1.3%
05/20/202044
 
1.3%
05/13/202043
 
1.3%
12/22/202043
 
1.3%
04/15/202042
 
1.3%
04/22/202042
 
1.3%
01/05/202142
 
1.3%
Other values (343)2850
86.7%

Most occurring characters

ValueCountFrequency (%)
09591
29.2%
28506
25.9%
/6574
20.0%
13598
 
10.9%
6769
 
2.3%
3752
 
2.3%
5727
 
2.2%
4708
 
2.2%
9599
 
1.8%
8553
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26296
80.0%
Other Punctuation6574
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09591
36.5%
28506
32.3%
13598
 
13.7%
6769
 
2.9%
3752
 
2.9%
5727
 
2.8%
4708
 
2.7%
9599
 
2.3%
8553
 
2.1%
7493
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/6574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09591
29.2%
28506
25.9%
/6574
20.0%
13598
 
10.9%
6769
 
2.3%
3752
 
2.3%
5727
 
2.2%
4708
 
2.2%
9599
 
1.8%
8553
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII32870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09591
29.2%
28506
25.9%
/6574
20.0%
13598
 
10.9%
6769
 
2.3%
3752
 
2.3%
5727
 
2.2%
4708
 
2.2%
9599
 
1.8%
8553
 
1.7%

notes
Categorical

HIGH CARDINALITY
MISSING

Distinct597
Distinct (%)45.5%
Missing2002
Missing (%)60.4%
Memory size26.0 KiB
We have been told vaccinations have begun in this system, but they have not released numbers for this week.
 
100
The number of tested people was mistakenly placed in the column for the total number of tests. This has been updated.
 
62
The number of recovered prisoners was updated. An earlier version included the numbers of deaths in the recovered count.
 
30
The number of tests conducted was mistakenly placed in the column for people tested. This has been updated.
 
27
Missouri's total cases omitted deaths and has been updated.
 
25
Other values (592)
1069 

Length

Max length867
Median length119
Mean length175.0792079
Min length17

Characters and Unicode

Total characters229879
Distinct characters83
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique499 ?
Unique (%)38.0%

Sample

1st rowWe have been told vaccinations have begun in this system, but they have not released numbers of people vaccinated for this week. The number of recovered prisoners was updated. An earlier version included the numbers of deaths in the recovered count.
2nd rowVaccines as of 6/16
3rd rowVaccines as of 6/18. 8329 prisoners received the one-shot Johnson & Johnson vaccine and were fully vaccinated.
4th row4191 prisoners have received the one-shot Johnson & Johnson vaccine. We remove 38 presumed positives from the recovered prisoners each week
5th rowThe vaccination numbers now include prisoners who have been released after being vaccinated and thus may appear to be a larger growth than it should. 25,850 prisoners have been fully vaccinated with the one-shot Johnson & Johnson vaccine. The state is also no longer reporting cases in community corrections, so we are adding in the last known number of cases there, 309. As a result, these case numbers are almost certainly an undercount.

Common Values

ValueCountFrequency (%)
We have been told vaccinations have begun in this system, but they have not released numbers for this week.100
 
3.0%
The number of tested people was mistakenly placed in the column for the total number of tests. This has been updated.62
 
1.9%
The number of recovered prisoners was updated. An earlier version included the numbers of deaths in the recovered count.30
 
0.9%
The number of tests conducted was mistakenly placed in the column for people tested. This has been updated.27
 
0.8%
Missouri's total cases omitted deaths and has been updated.25
 
0.8%
In early January 2020, North Dakota's Department of Corrections and Rehabilitation revised its testing figures, back to the beginning of the pandemic. We have updated our figures here accordingly.25
 
0.8%
For most weeks prior to Sept. 15, the prisoner cases and recoveries for Georgia inadvertently counted some cases in private prisons and county facilities twice. These figures have been corrected throughout.21
 
0.6%
Until early November, West Virginia’s Department of Corrections and Rehabilitation reported staff cases for juvenile facilities and jails combined with prisons and work release. After they began providing itemized numbers on staff cases on Nov. 3, we only included staff numbers from prisons and work release sites in our data releases. In our online tracker, we have gone back to the past weeks of data and estimated the staff breakdown based on the overall size of the staff for each sector.21
 
0.6%
The number of tests conducted was mistakenly placed in the column for the total number of people tested. This has been updated.19
 
0.6%
In May 2021, New Jersey's Department of Corrections revised much of its historic data for tests and cases among prisoners and staff. These figures revealed many new cases but also exclude prisoner cases in residential community release programs that were reported prior to August of 2020, including one death. We have retained the one death in a reentry center, but otherwise are now reporting only data for prisons. We have updated this week accordingly. We have been told vaccinations have begun in this system this week, but they have not released numbers.18
 
0.5%
Other values (587)965
29.1%
(Missing)2002
60.4%

Length

2021-06-30T15:35:16.533159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1733
 
4.6%
of1243
 
3.3%
have961
 
2.6%
in957
 
2.6%
this860
 
2.3%
and854
 
2.3%
to692
 
1.8%
for664
 
1.8%
staff638
 
1.7%
been628
 
1.7%
Other values (1302)28247
75.4%

Most occurring characters

ValueCountFrequency (%)
36193
15.7%
e27736
12.1%
t15361
 
6.7%
s14368
 
6.3%
a13308
 
5.8%
n13045
 
5.7%
r12356
 
5.4%
o12275
 
5.3%
i11536
 
5.0%
d8157
 
3.5%
Other values (73)65544
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter180675
78.6%
Space Separator36194
 
15.7%
Uppercase Letter5117
 
2.2%
Other Punctuation4628
 
2.0%
Decimal Number2771
 
1.2%
Dash Punctuation256
 
0.1%
Connector Punctuation70
 
< 0.1%
Open Punctuation55
 
< 0.1%
Close Punctuation55
 
< 0.1%
Final Punctuation35
 
< 0.1%
Other values (2)23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e27736
15.4%
t15361
 
8.5%
s14368
 
8.0%
a13308
 
7.4%
n13045
 
7.2%
r12356
 
6.8%
o12275
 
6.8%
i11536
 
6.4%
d8157
 
4.5%
h7265
 
4.0%
Other values (16)45268
25.1%
Uppercase Letter
ValueCountFrequency (%)
T904
17.7%
W488
9.5%
I462
9.0%
J444
8.7%
A406
7.9%
C376
 
7.3%
D331
 
6.5%
S287
 
5.6%
N233
 
4.6%
O229
 
4.5%
Other values (14)957
18.7%
Other Punctuation
ValueCountFrequency (%)
.2492
53.8%
,1215
26.3%
'442
 
9.6%
/257
 
5.6%
"98
 
2.1%
&96
 
2.1%
:19
 
0.4%
*5
 
0.1%
%3
 
0.1%
;1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2657
23.7%
1595
21.5%
0417
15.0%
3253
 
9.1%
4195
 
7.0%
5165
 
6.0%
8144
 
5.2%
9141
 
5.1%
6114
 
4.1%
790
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
-254
99.2%
1
 
0.4%
1
 
0.4%
Math Symbol
ValueCountFrequency (%)
=8
47.1%
+8
47.1%
>1
 
5.9%
Space Separator
ValueCountFrequency (%)
36193
> 99.9%
 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(55
100.0%
Close Punctuation
ValueCountFrequency (%)
)55
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_70
100.0%
Final Punctuation
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin185792
80.8%
Common44087
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e27736
14.9%
t15361
 
8.3%
s14368
 
7.7%
a13308
 
7.2%
n13045
 
7.0%
r12356
 
6.7%
o12275
 
6.6%
i11536
 
6.2%
d8157
 
4.4%
h7265
 
3.9%
Other values (40)50385
27.1%
Common
ValueCountFrequency (%)
36193
82.1%
.2492
 
5.7%
,1215
 
2.8%
2657
 
1.5%
1595
 
1.3%
'442
 
1.0%
0417
 
0.9%
/257
 
0.6%
-254
 
0.6%
3253
 
0.6%
Other values (23)1312
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII229841
> 99.9%
Punctuation37
 
< 0.1%
Latin 1 Sup1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36193
15.7%
e27736
12.1%
t15361
 
6.7%
s14368
 
6.3%
a13308
 
5.8%
n13045
 
5.7%
r12356
 
5.4%
o12275
 
5.3%
i11536
 
5.0%
d8157
 
3.5%
Other values (69)65506
28.5%
Punctuation
ValueCountFrequency (%)
35
94.6%
1
 
2.7%
1
 
2.7%
Latin 1 Sup
ValueCountFrequency (%)
 1
100.0%

Interactions

2021-06-30T15:34:53.951787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.051052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.124855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.217099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.322248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.434548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.517459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.585403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.673977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.754213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.835138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:54.916539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.002909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.074142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.141637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.213328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.299347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.385387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.469377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.550914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.626606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.701481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.772049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.859135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:55.947283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.031029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.112907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.190181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.264468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.343994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.433288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.529313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.620904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.707491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.798735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.886017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:56.978082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.065177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.154029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.242899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.329765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.424616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.519990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.606417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.688415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.778272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.867804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:57.952762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.040988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.126151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.222286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.311171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.398440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.485917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.572738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.662975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.754283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.836266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:58.916993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:59.003024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:59.087651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:59.838437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:34:59.932778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.012558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.099494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.183396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.264791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.345536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.425773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.511875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.599291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.668247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.747551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.840898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:00.933023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.019707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.112394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.198436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.277248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.356660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.441872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.524851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.610397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.701035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.788479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.855246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:01.931194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.017227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.101350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.181493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.266805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.349274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.424647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.507385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.589915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.669584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.749604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.833116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:02.920640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.010162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.079465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.168575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.264223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.349591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.430660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.507763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.758026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.855516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:03.943941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.034630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.119392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.198729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.280777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.359026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.443625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.528710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.615709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.699402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.779490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.860308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:04.935303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.021506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.110315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.197403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.283241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.379271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.478183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.562476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.654540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.740744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.826561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.910195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:05.999183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.083099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.169059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.259023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.339253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.420665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.502558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.591629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.677340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.755067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.837521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:06.923062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.008465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.089586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.174496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.253129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.342008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.429043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.518722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.602689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.684756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.770992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.854725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:07.940918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.022929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.107454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.191574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.271040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.354302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.434723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.721920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.825282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.907653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:08.989321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.069881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.159687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.249057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.319578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.398787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.495128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.586905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.676224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.768469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.853421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:09.933845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.018911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.110413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.198820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.289674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.387183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.484395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.552649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.627681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.726294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.822168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:10.914088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.007989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.102395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.190558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.274214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.365896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.455758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.548913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-30T15:35:11.642928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-30T15:35:16.682342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-30T15:35:16.856779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-30T15:35:17.024689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-30T15:35:17.197491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-30T15:35:17.430495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-30T15:35:11.848988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-30T15:35:12.098521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-30T15:35:12.330263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-30T15:35:12.605489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

nameabbreviationstaff_testsstaff_tests_with_multiplestotal_staff_casesstaff_recoveredtotal_staff_deathsstaff_partial_dosestaff_full_doseprisoner_testsprisoner_tests_with_multiplestotal_prisoner_casesprisoners_recoveredtotal_prisoner_deathsprisoners_partial_doseprisoners_full_doseas_of_datenotes
0AlabamaALNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1AlaskaAKNaNNaN332.0NaN0.0NaNNaNNaN38148.0NaNNaN5.0NaNNaN06/16/2021NaN
2ArizonaAZNaNNaN2785.02773.0NaNNaNNaN47758.0NaN12326.012246.064.0NaNNaN06/15/2021NaN
3ArkansasARNaNNaNNaNNaN4.0NaNNaNNaNNaN11416.011388.052.0NaNNaN06/16/2021We have been told vaccinations have begun in this system, but they have not released numbers of people vaccinated for this week. The number of recovered prisoners was updated. An earlier version included the numbers of deaths in the recovered count.
4CaliforniaCANaNNaN17002.016941.028.035115.033060.0128746.0NaN49375.048460.0224.070794.068805.006/15/2021NaN
5ColoradoCONaNNaN1948.0NaN0.03618.0NaN20591.0230798.08960.0NaN29.010541.0NaN06/16/2021NaN
6ConnecticutCTNaN86292.01690.01686.00.02697.0NaNNaN138227.04546.04503.019.04828.0NaN06/15/2021NaN
7DelawareDENaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8FloridaFLNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9GeorgiaGANaNNaN1745.01723.04.01650.0NaNNaNNaN3869.03768.093.026569.0NaN06/15/2021NaN

Last rows

nameabbreviationstaff_testsstaff_tests_with_multiplestotal_staff_casesstaff_recoveredtotal_staff_deathsstaff_partial_dosestaff_full_doseprisoner_testsprisoner_tests_with_multiplestotal_prisoner_casesprisoners_recoveredtotal_prisoner_deathsprisoners_partial_doseprisoners_full_doseas_of_datenotes
3305TennesseeTN0.0NaN0.0NaN0.0NaNNaN0.0NaN0.0NaN0.0NaNNaN03/26/2020NaN
3306TexasTXNaNNaN2.0NaN0.0NaNNaN25.0NaN1.0NaN0.0NaNNaN03/26/202025 tested = 15 pending, 1 positive, 9 negative.
3307UtahUTNaNNaNNaNNaN0.0NaNNaNNaNNaN0.0NaN0.0NaNNaN03/26/2020NaN
3308VermontVTNaNNaNNaNNaN0.0NaNNaNNaNNaN0.0NaN0.0NaNNaN03/26/2020NaN
3309VirginiaVANaNNaN0.0NaN0.0NaNNaN2.0NaN0.0NaN0.0NaNNaN03/26/2020NaN
3310WashingtonWANaNNaN4.0NaN0.0NaNNaN71.0NaN0.0NaN0.0NaNNaN03/26/2020NaN
3311West VirginiaWVNaNNaN0.0NaN0.0NaNNaNNaNNaN0.0NaN0.0NaNNaN03/26/2020back-filled data based on 4/1 responses. Until early November, West Virginia’s Department of Corrections and Rehabilitation reported staff cases for juvenile facilities and jails combined with prisons and work release. After they began providing itemized numbers on staff cases on Nov. 3, we only included staff numbers from prisons and work release sites in our data releases. In our online tracker, we have gone back to the past weeks of data and estimated the staff breakdown based on the overall size of the staff for each sector.
3312WisconsinWINaNNaN5.0NaN0.0NaNNaNNaNNaN0.0NaN0.0NaNNaN03/26/2020staff cases include those in adult institution + community corrections
3313WyomingWY0.0NaN0.0NaN0.0NaNNaN0.0NaN0.0NaN0.0NaNNaN03/26/2020NaN
3314FederalUSNaNNaN8.0NaN0.0NaNNaNNaNNaN10.0NaN0.0NaNNaN03/26/2020NaN